Color image segmentation using morphological clustering and fusion with automatic scale selection

In this paper, a color image segmentation method considering pairwise color projections is proposed. Each pairwise projection is analyzed according to an unsupervised morphological clustering which looks for the dominant colors of a 2D histogram. This leads to obtaining three segmentation maps combined by superposition after being simplified. The superposition process itself producing an over-segmentation of the image, a pairwise region merging is performed according to a similarity criterion up to a termination criterion. To fully automate the segmentation, an energy function is proposed to quantify the segmentation quality. The latter acts as a performance indicator and is used all over the segmentation to tune its parameters: the scale of the unsupervised morphological clustering and the termination criterion of region merging. Experimental results are conducted on a reference image database and comparisons with state-of-the-art algorithms.